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Detecting tiny objects in a high-resolution video is challenging because thevisual information is little and unreliable. Specifically, the challengeincludes very low resolution of the objects, MPEG artifacts due to compressionand a large searching area with many hard negatives. Tracking is equallydifficult because of the unreliable appearance, and the unreliable motionestimation. Luckily, we found that by combining this two challenging taskstogether, there will be mutual benefits. Following the idea, in this paper, wepresent a neural network model called the Recurrent Correlational Network,where detection and tracking are jointly performed over a multi-framerepresentation learned through a single, trainable, and end-to-end network. Theframework exploits a convolutional long short-term memory network for learninginformative appearance changes for detection, while the learned representationis shared in tracking for enhancing its performance. In experiments withdatasets containing images of scenes with small flying objects, such as birdsand unmanned aerial vehicles, the proposed method yielded consistentimprovements in detection performance over deep single-frame detectors andexisting motion-based detectors. Furthermore, our network performs as well asstate-of-the-art generic object trackers when it was evaluated as a tracker ona bird image dataset.

Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach